Awesome GitHub RepositoriesPlot Axis Customizers

Backtrader is a Python framework designed for the development, backtesting, and live execution of algorithmic trading strategies. It provides a comprehensive environment for quantitative finance, allowing users to simulate trading logic against historical market data or connect directly to brokerage platforms for automated real-time trading. The project distinguishes itself through a unified event-driven architecture that treats backtesting and live trading with the same API. This consistency is supported by a flexible data-feed abstraction layer that normalizes diverse financial sources, ena

Plotly.js is a JavaScript charting library and interactive graphing framework used to create web-based visualizations. It functions as a high-performance data visualization engine that utilizes both SVG for static elements and WebGL for hardware-accelerated rendering of large datasets and complex 3D plots. The library is distinguished by specialized toolkits for financial analysis, such as candlestick and OHLC charts, and geographic mapping tools for rendering choropleth and scatter maps with custom projections. It also supports complex scientific visualizations, including Sankey diagrams, pa

Optuna is a Python-based hyperparameter optimization framework designed to automate the search for optimal machine learning model configurations. It functions as a Bayesian optimization library that systematically tests parameter combinations to maximize or minimize objective functions, streamlining the model development process through iterative evaluation. The project distinguishes itself through a define-by-run dynamic construction model, which allows users to build complex, conditional search spaces using standard programming logic. Its architecture is highly modular, featuring a pluggabl

Seaborn is a Python library designed for statistical data visualization. It functions as a high-level interface built on the Matplotlib ecosystem, providing specialized routines to explore and communicate complex patterns within datasets. The framework enables users to generate informative graphics through automated statistical aggregation, multi-plot faceting, and integrated regression modeling. The library distinguishes itself through a declarative approach to data mapping, which translates raw inputs into visual properties like color, size, and position. It includes a robust statistical tr

This project is a scientific visualization guide and data visualization framework designed for creating high-quality 2D and 3D figures for academic journals and scientific publishing. It provides a structured approach to designing precise layouts, coordinate systems, and typography for complex scientific data. The project features a specialized print-ready PDF workflow and a CMYK print production workflow. These systems translate digital RGB colors into printer-specific CMYK profiles to ensure visual accuracy for physical hardcover and softcover printing. It also serves as a guide for SVG dat

jupyter-themes is a Jupyter Notebook theme manager and CSS interface customizer. It provides a command line tool to apply custom color schemes, fonts, and layout styles to notebook environments. The project includes a data visualization styling tool that synchronizes the aesthetic properties and color schemes of plotting libraries with the active interface theme. This ensures that data charts and figures remain visually consistent with the overall workspace theme.

SciencePlots is a Matplotlib style library and scientific plotting framework designed to automate the formatting of figures for academic journals and professional scientific publications. It provides a collection of visual presets and configuration rules for academic typography, layout, and resolution. The project features curated color-blind accessible palettes and figure formatters specifically designed to meet the strict submission standards of academic publishers. It includes specialized tools for professional figure styling and the rendering of non-Latin scripts for multilingual support.

This project is an educational resource and a collection of instructional materials for performing data manipulation and statistical analysis using Python. It provides a comprehensive set of guides and code examples for using the Pandas, NumPy, and Matplotlib libraries to analyze structured data. The resource includes a dedicated guide for reshaping, cleaning, and aggregating tabular data and time series via Pandas, alongside a reference for high-performance vectorized operations and linear algebra using NumPy. It also features tutorials for creating publication-quality charts, distribution p

react-vis is a declarative, component-based React data visualization library. It provides a framework of reusable building blocks for rendering interactive charts and graphs by mapping raw data to visual attributes such as position, color, and size. The system leverages D3 for its scaling and layout logic. The library is distinguished by its ability to handle complex data relationships, including hierarchical data via tree maps and circle packing, as well as multidimensional analysis using parallel axes and radar charts. It also supports network flow mapping to illustrate the volume and direc

fl_chart is a data visualization library and UI component framework for Flutter. It provides a system of reusable graphical widgets for creating interactive, customizable quantitative data visualizations. The framework supports a variety of chart types, including line, bar, pie, donut, scatter, radar, and candlestick views. It allows for the creation of complex visualizations such as layered data segments and financial charts. The library includes capabilities for interactivity and visual refinement, such as touch event handling, data tooltips, and state animations. It also provides tools fo

metrics-graphics is a data visualization library and declarative graphics framework designed to create principled data graphics and layouts. It functions as a statistical graphics engine that maps raw data to geometric shapes and structured objects to render complex, data-driven layouts. The toolkit specializes in rendering time-series data through line charts and scatterplots using a consistent layout system. It also provides capabilities for statistical distribution mapping, including the creation of rug plots to represent one-dimensional data density. The system covers a broad surface of

ggplot2 is a data visualization library for R based on a formal grammar of graphics. It provides a declarative plotting framework that allows users to create complex graphics by combining geometric objects, statistical summaries, and coordinate systems. The system is distinguished by a layered approach to composition, where visualizations are built incrementally by stacking independent geometric, statistical, and coordinate layers. It utilizes a hierarchical styling engine to manage non-data elements such as backgrounds, fonts, and margins, and includes a multi-panel faceting tool for splitti

ScottPlot is a cross-platform, high-performance charting library for .NET that renders interactive plots across desktop and web GUI frameworks including Windows Forms, WPF, MAUI, Avalonia, Blazor, and WinUI. It provides an optimized rendering engine capable of displaying millions of data points with interactive pan, zoom, and live data streaming, while also supporting image export to formats like PNG and SVG for file output, cloud applications, and notebooks. The library distinguishes itself through a comprehensive set of chart types including scatter, line, bar, pie, heatmap, financial, rada

Acest proiect este un curriculum educațional de machine learning și o platformă de învățare livrată prin Jupyter Notebooks interactive. Servește drept ghid cuprinzător pentru stăpânirea toolkit-ului de data science Python, oferind tutoriale structurate pentru calcul numeric, manipularea datelor tabelare și vizualizarea statistică. Curriculum-ul include ghiduri specifice de implementare pentru Scikit-Learn și un curs practic despre TensorFlow pentru construirea, antrenarea și deployment-ul rețelelor neuronale și a modelelor de computer vision. Acoperă procesul end-to-end de construire a modelelor predictive, de la formularea inițială a problemei și categorizarea sarcinilor până la deployment-ul modelelor prin interfețe web interactive. Proiectul acoperă o suprafață largă de capabilități, inclusiv calcul numeric cu array-uri multidimensionale, analiză exploratorie a datelor și rutine de preprocesare a datelor. Oferă fluxuri de lucru detaliate pentru învățarea supervizată și nesupervizată, pipeline-uri de machine learning automatizat, optimizarea hiperparametrilor și evaluarea modelelor folosind metrici de clasificare și cross-validation. Conținutul educațional este organizat ca o serie de notebook-uri care intercalează codul Python cu explicații narative pentru a documenta fluxurile de lucru în data science.

Live-Charts este o bibliotecă .NET de vizualizare a datelor care oferă o colecție de grafice, hărți și indicatori interactivi. Funcționează ca un motor de grafice în timp real și bibliotecă grafică multi-format concepută pentru a randa seturi de date complexe în cadrul aplicațiilor .NET. Biblioteca dispune de instrumente pentru crearea de dashboard-uri de date interactive capabile să exploreze seturi mari de date. Acest lucru este susținut de un sistem pentru zoom, pan și utilizarea mai multor axe de coordonate pentru a naviga prin sute de mii de puncte de date. Motorul de vizualizare suportă o varietate de formate, inclusiv bare, linii, hărți de căldură și hărți geografice. Include capabilități pentru monitorizarea datelor în timp real și dezvoltarea de dashboard-uri desktop pentru a urmări metricile și tendințele live.

ScrollableGraphView este o bibliotecă de vizualizare a datelor Swift și un framework de plotting pentru iOS utilizat pentru a randa seturi de date numerice discrete sub formă de grafice interactive. Oferă o componentă de interfață cu utilizatorul scrollabilă care vizualizează punctele de date folosind un sistem de coordonate cu layout-uri și stiluri configurabile. Framework-ul se caracterizează prin scalarea adaptivă a graficului, care ajustează automat axa verticală pentru a se potrivi punctelor de date vizibile pe măsură ce utilizatorul face scroll. Suportă randarea datelor în timp real, permițând vizualizărilor grafice să se actualizeze instantaneu pe măsură ce seturile de date subiacente se modifică prin tranziții animate. Biblioteca acoperă o varietate de tipuri de grafice, inclusiv grafice liniare, cu bare și cu puncte, și suportă plotting-ul multi-dataset pentru a afișa mai multe serii de date pe un singur grafic. Capabilitățile suplimentare includ etichetarea punctelor de date pe axa X, stilizarea personalizată a graficelor și utilizarea markerelor de linie de referință pentru a evidenția praguri sau valori de bază specifice.

Vega-Lite is a high-level declarative language for specifying interactive, multi-view visualizations. It compiles a concise JSON specification into a full Vega visualization, automatically inferring scales, axes, and legends from encoding declarations. The grammar-of-graphics encoding maps data fields to visual channels such as position, color, size, and shape, while a multi-view composition grammar enables layered, faceted, concatenated, and repeated layouts. Reactive parameter binding links named parameters to input widgets, selections, and expressions for dynamic updates. The project suppo

This is an interactive notebook-based course that teaches machine learning from Python fundamentals through deep learning and natural language processing. It uses real datasets and multiple frameworks within a structured, hands-on curriculum that combines concise explanations with executable code cells, built-in datasets, and embedded exercise checkpoints. Learning progresses through data preparation and exploration, classical machine learning workflows, computer vision with convolutional neural networks, and natural language processing with deep learning, all delivered as a cohesive progressi

This C++ data visualization library is a scientific plotting framework used to create 2D and 3D charts, network graphs, and geographic maps. It operates as a multi-backend graphics library, decoupling high-level plotting logic from low-level rendering engines to support various output backends. The project distinguishes itself with a dual-interface API, providing both a global functional interface for rapid prototyping and an object-oriented interface for precise control. It features a component-based layout engine for managing tiled grids and subplots, alongside a layered plot state that all